@article{VANDOOREN2020104313,
title = {Optimal Diesel engine calibration using convex modelling of Pareto frontiers},
journal = {Control Engineering Practice},
volume = {96},
pages = {104313},
year = {2020},
issn = {0967-0661},
doi = {https://doi.org/10.1016/j.conengprac.2020.104313},
url = {https://www.sciencedirect.com/science/article/pii/S0967066120300095},
author = {Stijn {van Dooren} and Camillo Balerna and Mauro Salazar and Alois Amstutz and Christopher H. Onder},
keywords = {Engine calibration, Optimal control, Pareto frontier, Convex modelling, Convex optimisation, Application-specific calibration},
abstract = {The optimal control of Diesel engines remains a challenging task. On the one hand, the number of control inputs is high, resulting in a large optimisation problem. On the other hand, low fuel consumption and low nitrogen oxides (NOx) emissions are conflicting objectives. This means there is no single best solution, but rather a set of Pareto optimal solutions. In this paper, we tackle the steady-state engine calibration problem by directly modelling the Pareto frontiers. This way, the degrees of freedom are reduced, resulting in a much simpler problem. Moreover, because the Pareto frontiers are (close to) convex, we are able to describe them by a convex function. We use lossless constraint relaxations to reformulate the problem as a convex optimisation problem. Solving this problem requires very little computation time and yields the globally optimal solution. The optimal control inputs can be retrieved from the optimal solution in a straightforward manner. We present experimental results to demonstrate the practical feasibility and effectiveness of the proposed approach. Furthermore, we show how the methodology can be readily extended to calculate application-specific calibrations that are tailored to typical in-use operation. Steady-state as well as transient measurements from the engine test-bench prove that significant fuel savings are achievable, while keeping the NOx emissions below the same limit.}
}

@article{MILLO2018807,
title = {Optimization of automotive diesel engine calibration using genetic algorithm techniques},
journal = {Energy},
volume = {158},
pages = {807-819},
year = {2018},
issn = {0360-5442},
doi = {https://doi.org/10.1016/j.energy.2018.06.044},
url = {https://www.sciencedirect.com/science/article/pii/S0360544218311095},
author = {Federico Millo and Pranav Arya and Fabio Mallamo},
keywords = {Diesel engine calibration, Genetic algorithm, Surrogate models, Multi objective optimization},
abstract = {Although the advancements in automotive diesel engines in the last two decades have resulted in the possibility of achieving better performance with lower pollutant emissions and fuel consumption, the increased complexity of the system and the high number of control parameters require the solution of optimization problems of high dimensionality. It is of crucial importance to identify suitable methodologies, which allow achieving the full exploitation of the potential of these powertrains. In this paper, an original methodology for optimizing the latest generation of common rail automotive diesel engines has been presented. Random optimization methods along with surrogate models were firstly used to generate a population of engine calibrations, which then served as an initial population to a specifically conceived Genetic Algorithm (GA) based optimizer, which was finally applied on a real data set for a particular engine operating point. The results were compared with a calibration optimized using a traditional local approach method. A simultaneous reduction of about 20% in NOX and 1% in Brake Specific Fuel Consumption was achieved, with no significant increase in other emissions. The methodology described in the paper has the potential to reduce the calibration time and effort by half, while obtaining better calibrations.}
}



@inproceedings{2019-01-1173,
author={Franken, Tim and Mauss, Fabian and Matrisciano, Andrea and Duggan, Alexander and Lehtiniemi, Harry and Borg, Anders},
title={Multi-Objective Optimization of Fuel Consumption and NO
<sub>x</sub>
 Emissions with Reliability Analysis Using a Stochastic Reactor Model},
booktitle={WCX SAE World Congress Experience},
publisher={SAE International},
month={apr},
year={2019},
doi={https://doi.org/10.4271/2019-01-1173},
url={https://doi.org/10.4271/2019-01-1173},
issn={0148-7191},
abstract={The introduction of a physics-based zero-dimensional stochastic reactor model combined with tabulated chemistry enables the simulation-supported development of future compression-ignited engines. The stochastic reactor model mimics mixture and temperature inhomogeneities induced by turbulence, direct injection and heat transfer. Thus, it is possible to improve the prediction of NOx emissions compared to common mean-value models. To reduce the number of designs to be evaluated during the simulation-based multi-objective optimization, genetic algorithms are proven to be an effective tool. Based on an initial set of designs, the algorithm aims to evolve the designs to find the best parameters for the given constraints and objectives. The extension by response surface models improves the prediction of the best possible Pareto Front, while the time of optimization is kept low. This work presents a novel methodology to couple the stochastic reactor model and the Non-dominated Sorting Genetic Algorithm. First, the stochastic reactor model is calibrated for 10 low, medium and high load operating points at various engine speeds. Second, each operating point is optimized to find the lowest fuel consumption and specific NOx emissions. The optimization input parameters are the temperature at intake valve closure, the compression ratio, the start of injection, the injection pressure and exhaust gas recirculation rate. Additionally, it is ensured that the maximum peak cylinder pressure and turbine inlet temperature are not exceeded. This enables a safe operation of the engine and exhaust aftertreatment system under the optimized conditions. Subsequently, a reliability analysis is performed to estimate the effect of off-nominal conditions on the objectives and constraints. The novel multi-objective optimization methodology has proven to deliver reasonable results. The zero-dimensional stochastic reactor model with tabulated chemistry is a fast running physics-based model that allow to run large optimization problems in a short amount of time. The combination with the reliability analysis also strengthens the confidence in the simulation-based optimized engine operation parameters.}
}
